class_docstr: A linear value function (baseline) based on features.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      reg_coeff (float): Regularization coefficient.
      name (str): Name of baseline.
docstr: Extract features from path.
  
  Args:
      path (list[dict]): Sample paths.
  
  Returns:
      numpy.ndarray: Extracted features.
docstr_tok: ["Extract","features","from","path",".","Args",":","path","(","list","[","dict","]",")",":","Sample","paths",".","Returns",":","numpy.ndarray",":","Extracted","features","."]
code: def _features(self, path):
      """Extract features from path.
  
      Args:
          path (list[dict]): Sample paths.
  
      Returns:
          numpy.ndarray: Extracted features.
  
      """
      obs = np.clip(
          path["observations"], self.lower_bound, self.upper_bound
      )
      length = len(path["observations"])
      al = np.arange(length).reshape(-1, 1) / 100.0
      return np.concatenate(
          [obs, obs**2, al, al**2, al**3, np.ones((length, 1))],
          axis=1,
      )
  
file_path: garage/src/garage/np/baselines/linear_feature_baseline.py
imports: import numpy
  from garage.np.baselines.baseline import Baseline
label_desc: Use image node_id 4 for calling the LinearFeatureBaseline obj's underlying member method code with example usage: obj._features(path) and returns: np